OpenRaiser/Intern-Atlas huggingface dataset:
A Fundamental Tradeoff Among Storage, Computation, and Communication for Distributed Computing over Star Network
Venue: arXiv cs.IT 2023
Year: 2023

Coded distributed computing can alleviate the communication load by leveraging the redundant storage and computation resources with coding techniques in distributed computing. In this paper, we study a MapReduce-type distributed computing framework over star topological network, where all the workers exchange information through a common access point. The optimal tradeoff among the normalized number of the stored files (storage load), computed intermediate values (computation load) and transmitted bits in the uplink and downlink (communication loads) are characterized. A coded computing scheme is proposed to achieve the Pareto-optimal tradeoff surface, in which the access point only needs to perform simple chain coding between the signals it receives, and information-theretical bound matching the surface is also provided.
Look Beyond Bias with Entropic Adversarial Data Augmentation
Venue: arXiv cs.LG 2023
Year: 2023

Deep neural networks do not discriminate between spurious and causal patterns, and will only learn the most predictive ones while ignoring the others. This shortcut learning behaviour is detrimental to a network's ability to generalize to an unknown test-time distribution in which the spurious correlations do not hold anymore. Debiasing methods were developed to make networks robust to such spurious biases but require to know in advance if a dataset is biased and make heavy use of minority counterexamples that do not display the majority bias of their class. In this paper, we argue that such samples should not be necessarily needed because the ''hidden'' causal information is often also contained in biased images. To study this idea, we propose 3 publicly released synthetic classification benchmarks, exhibiting predictive classification shortcuts, each of a different and challenging nature, without any minority samples acting as counterexamples. First, we investigate the effectiveness of several state-of-the-art strategies on our benchmarks and show that they do not yield satisfying results on them. Then, we propose an architecture able to succeed on our benchmarks, despite their unusual properties, using an entropic adversarial data augmentation training scheme. An encoder-decoder architecture is tasked to produce images that are not recognized by a classifier, by maximizing the conditional entropy of its outputs, and keep as much as possible of the initial content. A precise control of the information destroyed, via a disentangling process, enables us to remove the shortcut and leave everything else intact. Furthermore, results competitive with the state-of-the-art on the BAR dataset ensure the applicability of our method in real-life situations.
Practitioners' Expectations on Code Completion
Venue: arXiv cs.SE 2023
Year: 2023

Code completion has become a common practice for programmers during their daily programming activities. It aims at automatically predicting the next tokens or lines that the programmers tend to use. A good code completion tool can substantially save keystrokes and improve the programming efficiency for programmers. Recently, various techniques for code completion have been proposed for usage in practice. However, it is still unclear what are practitioners' expectations on code completion and whether existing research has met their demands. To fill the gap, we perform an empirical study by first interviewing 15 practitioners and then surveying 599 practitioners from 18 IT companies about their expectations on code completion. We then compare the practitioners' demands with current research via conducting a literature review of papers on code completion published in premier publication venues from 2012 to 2022. Based on the comparison, we highlight the directions desirable for researchers to invest efforts towards developing code completion techniques for meeting practitioners' expectations.
Actor-Director-Critic: A Novel Deep Reinforcement Learning Framework
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we propose actor-director-critic, a new framework for deep reinforcement learning. Compared with the actor-critic framework, the director role is added, and action classification and action evaluation are applied simultaneously to improve the decision-making performance of the agent. Firstly, the actions of the agent are divided into high quality actions and low quality actions according to the rewards returned from the environment. Then, the director network is trained to have the ability to discriminate high and low quality actions and guide the actor network to reduce the repetitive exploration of low quality actions in the early stage of training. In addition, we propose an improved double estimator method to better solve the problem of overestimation in the field of reinforcement learning. For the two critic networks used, we design two target critic networks for each critic network instead of one. In this way, the target value of each critic network can be calculated by taking the average of the outputs of the two target critic networks, which is more stable and accurate than using only one target critic network to obtain the target value. In order to verify the performance of the actor-director-critic framework and the improved double estimator method, we applied them to the TD3 algorithm to improve the TD3 algorithm. Then, we carried out experiments in multiple environments in MuJoCo and compared the experimental data before and after the algorithm improvement. The final experimental results show that the improved algorithm can achieve faster convergence speed and higher total return.
CHRONOS: Time-Aware Zero-Shot Identification of Libraries from Vulnerability Reports
Venue: arXiv cs.SE 2023
Year: 2023

Tools that alert developers about library vulnerabilities depend on accurate, up-to-date vulnerability databases which are maintained by security researchers. These databases record the libraries related to each vulnerability. However, the vulnerability reports may not explicitly list every library and human analysis is required to determine all the relevant libraries. Human analysis may be slow and expensive, which motivates the need for automated approaches. Researchers and practitioners have proposed to automatically identify libraries from vulnerability reports using extreme multi-label learning (XML). While state-of-the-art XML techniques showed promising performance, their experiment settings do not practically fit what happens in reality. Previous studies randomly split the vulnerability reports data for training and testing their models without considering the chronological order of the reports. This may unduly train the models on chronologically newer reports while testing the models on chronologically older ones. However, in practice, one often receives chronologically new reports, which may be related to previously unseen libraries. Under this practical setting, we observe that the performance of current XML techniques declines substantially, e.g., F1 decreased from 0.7 to 0.28 under experiments without and with consideration of chronological order of vulnerability reports. We propose a practical library identification approach, namely CHRONOS, based on zero-shot learning. The novelty of CHRONOS is three-fold. First, CHRONOS fits into the practical pipeline by considering the chronological order of vulnerability reports. Second, CHRONOS enriches the data of the vulnerability descriptions and labels using a carefully designed data enhancement step. Third, CHRONOS exploits the temporal ordering of the vulnerability reports using a cache to prioritize prediction of...
Towards High-Definition Maps: a Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity
Venue: arXiv cs.RO 2023
Year: 2023

High-Definition (HD) maps are needed for robust navigation of autonomous vehicles, limited by the on-board storage capacity. To solve this, we propose a novel framework, Environment-Aware Normal Distributions Transform (EA-NDT), that significantly improves compression of standard NDT map representation. The compressed representation of EA-NDT is based on semantic-aided clustering of point clouds resulting in more optimal cells compared to grid cells of standard NDT. To evaluate EA-NDT, we present an open-source implementation that extracts planar and cylindrical primitive features from a point cloud and further divides them into smaller cells to represent the data as an EA-NDT HD map. We collected an open suburban environment dataset and evaluated EA-NDT HD map representation against the standard NDT representation. Compared to the standard NDT, EA-NDT achieved consistently at least 1.5x higher map compression while maintaining the same descriptive capability. Moreover, we showed that EA-NDT is capable of producing maps with significantly higher descriptivity score when using the same number of cells than the standard NDT.
Exploring the Use of WebAssembly in HPC
Venue: arXiv cs.DC 2023
Year: 2023

Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute them. However, their adoption and usage in HPC systems faces several challenges. These include difficulties in unprivileged running and building of scientific application container images directly on HPC resources, increasing heterogeneity of HPC architectures, and access to specialized networking libraries available only on HPC systems. These challenges of container-based HPC application development closely align with the several advantages that a new universal intermediate binary format called WebAssembly (Wasm) has to offer. These include a lightweight userspace isolation mechanism and portability across operating systems and processor architectures. In this paper, we explore the usage of Wasm as a distribution format for MPI-based HPC applications. To this end, we present MPIWasm, a novel Wasm embedder for MPI-based HPC applications that enables high-performance execution of Wasm code, has low-overhead for MPI calls, and supports high-performance networking interconnects present on HPC systems. We evaluate the performance and overhead of MPIWasm on a production HPC system and AWS Graviton2 nodes using standardized HPC benchmarks. Results from our experiments demonstrate that MPIWasm delivers competitive native application performance across all scenarios. Moreover, we observe that Wasm binaries are 139.5x smaller on average as compared to the statically-linked binaries for the different standardized benchmarks.
API Entity and Relation Joint Extraction from Text via Dynamic Prompt-tuned Language Model
Venue: arXiv cs.SE 2023
Year: 2023

Extraction of Application Programming Interfaces (APIs) and their semantic relations from unstructured text (e.g., Stack Overflow) is a fundamental work for software engineering tasks (e.g., API recommendation). However, existing approaches are rule-based and sequence-labeling based. They must manually enumerate the rules or label data for a wide range of sentence patterns, which involves a significant amount of labor overhead and is exacerbated by morphological and common-word ambiguity. In contrast to matching or labeling API entities and relations, this paper formulates heterogeneous API extraction and API relation extraction task as a sequence-to-sequence generation task, and proposes AERJE, an API entity-relation joint extraction model based on the large pre-trained language model. After training on a small number of ambiguous but correctly labeled data, AERJE builds a multi-task architecture that extracts API entities and relations from unstructured text using dynamic prompts. We systematically evaluate AERJE on a set of long and ambiguous sentences from Stack Overflow. The experimental results show that AERJE achieves high accuracy and discrimination ability in API entity-relation joint extraction, even with zero or few-shot fine-tuning.
IronForge: An Open, Secure, Fair, Decentralized Federated Learning
Venue: arXiv cs.LG 2023
Year: 2023

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the over-dependence on a central coordinator, and the lack of an open and fair incentive mechanism collectively hinder its further development. We propose \textsc{IronForge}, a new generation of FL framework, that features a Directed Acyclic Graph (DAG)-based data structure and eliminates the need for central coordinators to achieve fully decentralized operations. \textsc{IronForge} runs in a public and open network, and launches a fair incentive mechanism by enabling state consistency in the DAG, so that the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of \textsc{IronForge}. Experimental results based on a newly developed testbed FLSim highlight the superiority of \textsc{IronForge} to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, \textsc{IronForge} is the first secure and fully decentralized FL framework that can be applied in open networks with realistic network and training settings.
Causal Inference for Recommendation: Foundations, Methods and Applications
Venue: arXiv cs.IR 2023
Year: 2023

Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recommendations based on correlations found in the data. However, relying solely on correlation without considering the underlying causal mechanism may lead to various practical issues such as fairness, explainability, robustness, bias, echo chamber and controllability problems. Therefore, researchers in related area have begun incorporating causality into recommendation systems to address these issues. In this survey, we review the existing literature on causal inference in recommender systems. We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems. Finally, we discuss open problems and future directions in the field of causal inference for recommendations.
Conversational Turn-taking as a Stochastic Process on Networks
Venue: arXiv cs.SI 2023
Year: 2023

Understanding why certain individuals work well (or poorly) together as a team is a key research focus in the psychological and behavioral sciences and a fundamental problem for team-based organizations. Nevertheless, we have a limited ability to predict the social and work-related dynamics that will emerge from a given combination of team members. In this work, we model vocal turn-taking behavior within conversations as a parametric stochastic process on a network composed of the team members. More precisely, we model the dynamic of exchanging the `speaker token' among team members as a random walk in a graph that is driven by both individual level features and the conversation history. We fit our model to conversational turn-taking data extracted from audio recordings of multinational student teams during undergraduate engineering design internships. Through this real-world data we validate the explanatory power of our model and we unveil statistically significant differences in speaking behaviors between team members of different nationalities.
Benchmarking Robustness in Neural Radiance Fields
Venue: arXiv cs.CV 2023
Year: 2023

Neural Radiance Field (NeRF) has demonstrated excellent quality in novel view synthesis, thanks to its ability to model 3D object geometries in a concise formulation. However, current approaches to NeRF-based models rely on clean images with accurate camera calibration, which can be difficult to obtain in the real world, where data is often subject to corruption and distortion. In this work, we provide the first comprehensive analysis of the robustness of NeRF-based novel view synthesis algorithms in the presence of different types of corruptions. We find that NeRF-based models are significantly degraded in the presence of corruption, and are more sensitive to a different set of corruptions than image recognition models. Furthermore, we analyze the robustness of the feature encoder in generalizable methods, which synthesize images using neural features extracted via convolutional neural networks or transformers, and find that it only contributes marginally to robustness. Finally, we reveal that standard data augmentation techniques, which can significantly improve the robustness of recognition models, do not help the robustness of NeRF-based models. We hope that our findings will attract more researchers to study the robustness of NeRF-based approaches and help to improve their performance in the real world.
Analogical Relevance Index
Venue: arXiv cs.LG 2023
Year: 2023

Focusing on the most significant features of a dataset is useful both in machine learning (ML) and data mining. In ML, it can lead to a higher accuracy, a faster learning process, and ultimately a simpler and more understandable model. In data mining, identifying significant features is essential not only for gaining a better understanding of the data but also for visualization. In this paper, we demonstrate a new way of identifying significant features inspired by analogical proportions. Such a proportion is of the form of "a is to b as c is to d", comparing two pairs of items (a, b) and (c, d) in terms of similarities and dissimilarities. In a classification context, if the similarities/dissimilarities between a and b correlate with the fact that a and b have different labels, this knowledge can be transferred to c and d, inferring that c and d also have different labels. From a feature selection perspective, observing a huge number of such pairs (a, b) where a and b have different labels provides a hint about the importance of the features where a and b differ. Following this idea, we introduce the Analogical Relevance Index (ARI), a new statistical test of the significance of a given feature with respect to the label. ARI is a filter-based method. Filter-based methods are ML-agnostic but generally unable to handle feature redundancy. However, ARI can detect feature redundancy. Our experiments show that ARI is effective and outperforms well-known methods on a variety of artificial and some real datasets.
Deep Learning based Multi-Label Image Classification of Protest Activities
Venue: arXiv cs.CV 2023
Year: 2023

With the rise of internet technology amidst increasing rates of urbanization, sharing information has never been easier thanks to globally-adopted platforms for digital communication. The resulting output of massive amounts of user-generated data can be used to enhance our understanding of significant societal issues particularly for urbanizing areas. In order to better analyze protest behavior, we enhanced the GSR dataset and manually labeled all the images. We used deep learning techniques to analyze social media data to detect social unrest through image classification, which performed good in predict multi-attributes, then also used map visualization to display protest behaviors across the country.
Explaining Deep Models through Forgettable Learning Dynamics
Venue: arXiv cs.CV 2023
Year: 2023

Even though deep neural networks have shown tremendous success in countless applications, explaining model behaviour or predictions is an open research problem. In this paper, we address this issue by employing a simple yet effective method by analysing the learning dynamics of deep neural networks in semantic segmentation tasks. Specifically, we visualize the learning behaviour during training by tracking how often samples are learned and forgotten in subsequent training epochs. This further allows us to derive important information about the proximity to the class decision boundary and identify regions that pose a particular challenge to the model. Inspired by this phenomenon, we present a novel segmentation method that actively uses this information to alter the data representation within the model by increasing the variety of difficult regions. Finally, we show that our method consistently reduces the amount of regions that are forgotten frequently. We further evaluate our method in light of the segmentation performance.
Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations
Venue: arXiv cs.CY 2023
Year: 2023

Generative language models have improved drastically, and can now produce realistic text outputs that are difficult to distinguish from human-written content. For malicious actors, these language models bring the promise of automating the creation of convincing and misleading text for use in influence operations. This report assesses how language models might change influence operations in the future, and what steps can be taken to mitigate this threat. We lay out possible changes to the actors, behaviors, and content of online influence operations, and provide a framework for stages of the language model-to-influence operations pipeline that mitigations could target (model construction, model access, content dissemination, and belief formation). While no reasonable mitigation can be expected to fully prevent the threat of AI-enabled influence operations, a combination of multiple mitigations may make an important difference.
CARD: Semantic Segmentation with Efficient Class-Aware Regularized Decoder
Venue: arXiv cs.CV 2023
Year: 2023

Semantic segmentation has recently achieved notable advances by exploiting "class-level" contextual information during learning. However, these approaches simply concatenate class-level information to pixel features to boost the pixel representation learning, which cannot fully utilize intra-class and inter-class contextual information. Moreover, these approaches learn soft class centers based on coarse mask prediction, which is prone to error accumulation. To better exploit class level information, we propose a universal Class-Aware Regularization (CAR) approach to optimize the intra-class variance and inter-class distance during feature learning, motivated by the fact that humans can recognize an object by itself no matter which other objects it appears with. Moreover, we design a dedicated decoder for CAR (CARD), which consists of a novel spatial token mixer and an upsampling module, to maximize its gain for existing baselines while being highly efficient in terms of computational cost. Specifically, CAR consists of three novel loss functions. The first loss function encourages more compact class representations within each class, the second directly maximizes the distance between different class centers, and the third further pushes the distance between inter-class centers and pixels. Furthermore, the class center in our approach is directly generated from ground truth instead of from the error-prone coarse prediction. CAR can be directly applied to most existing segmentation models during training, and can largely improve their accuracy at no additional inference overhead. Extensive experiments and ablation studies conducted on multiple benchmark datasets demonstrate that the proposed CAR can boost the accuracy of all baseline models by up to 2.23% mIOU with superior generalization ability. CARD outperforms SOTA approaches on multiple benchmarks with a highly efficient architecture.